Early Detection and Diagnosis of Oral Cancer Using Deep Neural Network

K. Vinay Kumar; Sumanaswini Palakurthy; Sri Harsha Balijadaddanala; Sharmila Reddy  Pappula; Anil Kumar Lavudya

doi:10.69996/jcai.2024008

Authors

K. Vinay Kumar Department of CSE, Kakatiya Institute of Technology and Science, Warangal, Telangana,506001, India Author
Sumanaswini Palakurthy Department of CSE, Kakatiya Institute of Technology and Science, Warangal, Telangana,506001, India Author
Sri Harsha Balijadaddanala Department of CSE, Kakatiya Institute of Technology and Science, Warangal, Telangana,506001, India Author
Sharmila Reddy Pappula Department of CSE, Kakatiya Institute of Technology and Science, Warangal, Telangana,506001, India Author
Anil Kumar Lavudya Department of CSE, Kakatiya Institute of Technology and Science, Warangal, Telangana,506001, India Author

DOI:

https://doi.org/10.69996/jcai.2024008

Keywords:

Convolutional Neural Network (CNN), Oral squamous cell carcinoma (OSCC), oral cancer detection, Deep Learning (DL), Inception-Resnet-V2

Abstract

Oral squamous cell carcinoma (OSCC) is a malignancy that destroys the ability of the tissues surrounding the mouth to develop layers and membranes. Automated early diagnosis of oral histopathological images has allowed for the successful diagnosis of oral cancer, thanks to recent advancements in Deep Learning (DL) for biomedical image classification. By using a convolutional neural network (CNN) model based on deep learning for the initial analysis of oral squamous cell carcinoma (OSCC), this work aims to automate the classification of benign and malignant oral biopsy histopathological images. For this study, the CNN model Inception-Resnet-V2 is selected using the transfer learning approach. To enhance OSCC detection, additional layers are incorporated into this pretrained model. By mining a repository of oral cancer histopathology images, we can gauge how well these tweaked models perform. We examine the modified structure of the pre-trained Inception-Resnet-V2 model and suggest a DL-CNN model that uses it. With an accuracy of 91.78%, it has outperformed in terms of performance metrics

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